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Double Oracle Neural Architecture Search for Game Theoretic Deep Learning Models

IEEE Transactions on Image Processing 2025年 34卷 2463-2472页

作者： Aung, Aye Phyu Phyu Wang, Xinrun Wang, Ruiyu Chan, Hau An, Bo Li, Xiaoli Senthilnath, J. Singapore138632 Singapore Singapore Management University School of Computing and Information Systems Bras Basah 188065 Singapore KTH Royal Institute of Technology Division of Robotics Perception and Learning Stockholm100 44 Sweden University of Nebraska-Lincoln School of Computing LincolnNE68588 United States Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore A STAR Centre for Frontier AI Research Singapore138632 Singapore

In this paper, we propose a new approach to train deep learning models using game theory concepts including Generative Adversarial Networks (GANs) and Adversarial Training (AT) where we deploy a double-oracle framework using best response oracles. GAN is essentially a two-player zero-sum game between the generator and the discriminator. The same concept can be applied to AT with attacker and classifier as players. Training these models is challenging as a pure Nash equilibrium may not exist and even finding the mixed Nash equilibrium is difficult as training algorithms for both GAN and AT have a large-scale strategy space. Extending our preliminary model DO-GAN, we propose the methods to apply the double oracle framework concept to Adversarial Neural Architecture Search (NAS for GAN) and Adversarial Training (NAS for AT) algorithms. We first generalize the players' strategies as the trained models of generator and discriminator from the best response oracles. We then compute the meta-strategies using a linear program. For scalability of the framework where multiple network models of best responses are stored in the memory, we prune the weakly-dominated players' strategies to keep the oracles from becoming intractable. Finally, we conduct experiments on MNIST, CIFAR-10 and TinyImageNet for DONAS-GAN. We also evaluate the robustness under FGSM and PGD attacks on CIFAR-10, SVHN and TinyImageNet for DONAS-AT. We show that all our variants have significant improvements in both subjective qualitative evaluation and quantitative metrics, compared with their respective base architectures. © 1992-2012 IEEE.

关键词： Generative adversarial networks

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Fuzzy-Based Optimization Techniques for Segmenting the Tumors in Multimodal MRI Images

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Operations Research Forum 2025年第1期6卷 1-27页

作者： Alagarsamy, Saravanan Nagarajan, D. Govindaraj, Vishnuvardhan Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Rajiv Gandhi Salai (OMR) Tamilnadu Chennai India Department of Mathematics Rajalakshmi Institute of Technology Chennai India Division of Data Science of School of Computing Science and Education VIT Bhopal University Kothrikalan Madhya Pradesh Sehore 466114 India

Accurate brain tumor prediction is essential when approaching the field of health care;wherever accuracy in decision-making is crucial, the issues also need to be resolved right away. Many artificial intelligence and machine learning-based techniques have been developed in the last couple of years in the field of healthcare. The intention of this work is to create an algorithm that combines the features of Crow Search (CS) and Interval Type-II Fuzzy Logic System (IT2FLS) algorithms to distinguish the area of tumor from complex brain tissues. The ability of oncologists to make decisions is critical for any therapy sequence to be successful, and the methodology proposed in the research work considerably influences a conclusion through technology interruption. The suggested approach is flexible enough to work with a diversity of image sequences found in the BRATS Challenge 2020 dataset that presents varying degrees of obstacles, challenges, and difficulties in locating the anomalous regions, and it produces improved demarcation results that have been instinctively evaluated and supported by Dice score (98 ± 1.3), specificity (98 ± 1.4), and sensitivity (98 ± 1.7) as average metrics. The motive of this study is the enhancement of the visual perception of oncologists, which gives them improved insight into and comprehension of the condition of the patient. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Crow Search (CS) Interval Type-II Fuzzy Logic System (IT2FLS) Magnetic Resonance Imaging (MRI) Tumor segmentation

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Building Privacy-Preserving Medical Text Models With a Pretrained Transformer

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IEEE Internet of Things Journal 2025年第9期12卷 11529-11538页

作者： Shafiq, Muhammad Ren, Lijing Srivastava, Gautam Zhang, Denghui Bourouis, Sami Gadekallu, Thippa Reddy Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou511370 China Institute of Artificial Intelligence Guangdong Mechanical & Electrical Polytechnic Guangzhou510515 China Department of Math and Computer Science Brandon University BrandonMBR7A 6A9 Canada Research Centre for Interneural Computing China Medical University Taichung40402 Taiwan Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura140401 India Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan250013 China Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Division of Research and Development Lovely Professional University Phagwara144411 India Center of Research Impact and Outcome Chitkara University Rajpura140401 India

The rapid advancement of big data and artificial intelligence (AI) in healthcare heightens the urgency for accurate medical text sentiment analysis. The privacy protection of medical data has been a crucial concern due to its sensitivity. The Internet of Medical Things (IoMT) facilitates large-scale data collection at lower cost, enabling precision medicine. However, decentralized IoMT poses novel challenges to centralized standard encryption schemes. In this article, we propose a novel approach to building privacy-preserving sentiment models with a generative pretrained transformer (GPT). We first convert sensitive medical text data into noise-like and distributed one-hot images. Then, we introduce visual cryptography (VC) for lightweight and secure transmission of medical text across public networks in resource-limited IoMT devices. We adopt a cross-domain sentiment analysis framework that finetunes transformer-based language models for accurate sentiment analysis instead of training GPT in sentiment analysis from scratch. Experimental results show that the proposed approach improves the accuracy and effectiveness of sentiment analysis while maintaining privacy, thereby addressing a significant gap in biomedical text analysis. © 2014 IEEE.

关键词： Differential privacy

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Multi-Agent DRL for Queue-Aware Task Offloading in Hierarchical MEC-Enabled Air-Ground Networks

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Hevesli, Muhammet Seid, Abegaz Mohammed Erbad, Aiman Abdallah, Mohamed Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Qatar University College of Engineering Doha Qatar

Mobile edge computing (MEC)-enabled air-ground networks advance 6G wireless networks by utilizing aerial base stations (ABSs) such as unmanned aerial vehicles (UAVs) and high altitude platform stations (HAPS) to provide dynamic services to ground IoT devices (IoTDs). These IoTDs support real-time applications like multimedia and Metaverse services, which demand high computational resources and strict quality of service (QoS) guarantees, specifically in terms of latency and efficient task queue management. However, IoTDs often face constraints in energy and computational power, requiring efficient queue management and task scheduling to maintain QoS. To address these challenges, UAVs and HAPS are deployed to supplement the computational limitations of IoTDs by offloading tasks for distributed processing. Due to UAVs' resource limitations, particularly in terms of power and coverage area, HAPS are used to enhance their capabilities and extend coverage. Overloaded UAVs may relay tasks to HAPS, creating a multi-tier computing system. This paper addresses the overall energy minimization problem in the MEC-enabled air-ground integrated network (MAGIN) by optimizing UAV trajectories, computing resource allocation, and queue-aware task offloading decisions. The optimization problem is highly complex due to the nonconvex and nonlinear nature of this hierarchical system, which traditional methods cannot effectively solve. To tackle this, we reformulate the problem as a multi-agent Markov decision process (MDP) with continuous action spaces and heterogeneous agents. We propose a novel variant of multi-agent proximal policy optimization (MAPPO) with Beta distribution (MAPPO-BD) to solve this problem. Extensive simulations show that MAPPO-BD significantly outperforms other baselines, achieving superior energy savings and efficient resource management in MAGIN, while adhering to constraints related to queue delays and edge computing capabilities. © 2025 IEEE.

关键词： Quality of service

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Simultaneous Multi-Adulterant Detection in Milk Using an In-house Developed Multiplexed Microfluidic Paper-Based Analytical Device (M-µPAD)

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Sensing and Imaging 2025年第1期26卷 1-20页

作者： Ahsan, Jannat Riaz, Kashif Samad, Hudair Mehmood, Muhammad Qasim Khelif, Abdelkrim Department of Electrical Engineering Information Technology University of the Punjab (ITU) Lahore Pakistan Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Advanced Manufacturing Research Centre University of Sheffield Rotherham UK

Milk adulteration poses a significant challenge in developing countries, impacting food safety and public health. This study introduces a multiplexed microfluidic paper-based analytical device (M-µPAD) for rapid and simultaneous detection of multiple milk adulterants. The M-µPAD, fabricated using readily available materials like filter paper and wax crayons, eliminates the need for specialized equipment. The device demonstrates linear relationships between colorimetric response and adulterant concentration for urea (10–200 mg/dL), starch (20-1000 mg/dL), and detergent (100–3000 mg/dL), with detection limits of 11.1, 22.4, and 120.8 mg/dL, respectively. Requiring only 40 µL of reagents and 80 µL of milk sample, the M-µPAD achieves over 95% accuracy and reproducibility with RSD values below 4%. This affordable, in-house fabricated platform offers a promising solution for milk quality assessment in resource-limited settings, addressing the need for accessible and efficient adulterant detection methods.

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Leveraging Digital Twin and Machine Learning Techniques for Anomaly Detection in Power Electronics Dominated Grid

arXiv

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arXiv 2025年

作者： Idrisov, Ildar Okeke, Divine Albaseer, Abdullatif Abdallah, Mohamed Ibanez, Federico M. Center for Energy Science and Technology Skolkovo Institute of Science and Technology Moscow Russia Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

Modern power grids are transitioning towards power electronics-dominated grids (PEDG) due to the increasing integration of renewable energy sources and energy storage systems. This shift introduces complexities in grid operation and increases vulnerability to cyberattacks. This research explores the application of digital twin (DT) technology and machine learning (ML) techniques for anomaly detection in PEDGs. A DT can accurately track and simulate the behavior of the physical grid in real-time, providing a platform for monitoring and analyzing grid operations, with extended amount of data about dynamic power flow along the whole power system. By integrating ML algorithms, the DT can learn normal grid behavior and effectively identify anomalies that deviate from established patterns, enabling early detection of potential cyberattacks or system faults. This approach offers a comprehensive and proactive strategy for enhancing cybersecurity and ensuring the stability and reliability of PEDGs. © 2025, CC BY.

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CenFormer: Transformer-based Network from Centroid Generation for Point Cloud Completion

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Phong Nguyen, Tran Thanh Phung, Son Lam Gopaldasani, Vinod Whitelaw, Jane Le, Hoang Thanh Bouzerdoum, Abdesselam University of Wollongong Faculty of Engineering and Information Sciences Wollongong Australia University of Wollongong Faculty of the Arts Social Sciences and Humanities Wollongong Australia Hamad Bin Khalifa University Division of Information and Computing Technology Doha Qatar

Point clouds captured from 3D scanners are often sparse and incomplete due to occlusions, limited viewpoints, and sensor constraints. These limitations hinder applications in robotics, autonomous navigation, and augmented reality. Hence, point cloud completion is crucial for generating reliable 3D object representations. Existing methods often struggle to capture structural patterns effectively, which leads to low-quality reconstructions. To address these challenges, we propose Centroid Transformer (CenFormer), a novel transformer-based network for point cloud completion. CenFormer introduces two distinct types of centroids, namely Preserved and Dispersed, to facilitate fine-grained reconstruction. The proposed design includes three innovations: i) a Centroid Generation Block to aggregate features for preserved centroids, ii) a Centroid Dispersion Block to predict offsets for dispersed centroids, and iii) a Fine-grained Point Generation Block to refine local patterns around centroids. These components jointly enable the network to effectively capture local structural details and strategically target missing regions for fine-grained 3D shape reconstruction. Experiments on various benchmark datasets demonstrate that CenFormer significantly outperforms state-of-the-art methods in both visualization results and quantitative metrics. © 2020 IEEE.

关键词： 3D Object Reconstruction Deep Learning Point Cloud Completion Point Cloud Understanding Transformer

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VMGuard: Reputation-Based Incentive Mechanism for Poisoning Attack Detection in Vehicular Metaverse

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IEEE Transactions on Vehicular technology 2025年

作者： Lotfi, Ismail Qaraqe, Marwa Ghrayeb, Ali Niyato, Dusit Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Nanyang Technological University School of Computer Science and Engineering Singapore Singapore

The vehicular Metaverse represents an emerging paradigm that merges vehicular communications with virtual environments, integrating real-world data to enhance in-vehicle services. However, this integration faces critical security challenges, particularly in the data collection layer where malicious sensing IoT (SIoT) devices can compromise service quality through data poisoning attacks. The security aspects of the Metaverse services should be well addressed both when creating the digital twins of the physical systems and when delivering the virtual service to the vehicular Metaverse users (VMUs). This paper introduces vehicular Metaverse guard (VMGuard), a novel four-layer security framework that protects vehicular Metaverse systems from data poisoning attacks. Specifically, when the virtual service providers (VSPs) collect data about physical environment through SIoT devices in the field, the delivered content might be tampered. Malicious SIoT devices with moral hazard might have private incentives to provide poisoned data to the VSP to degrade the service quality (QoS) and user experience (QoE) of the VMUs. The proposed framework implements a reputation-based incentive mechanism that leverages user feedback and subjective logic modeling to assess the trustworthiness of participating SIoT devices. More precisely, the framework entails the use of reputation scores assigned to participating SIoT devices based on their historical engagements with the VSPs. These scores are calculated from feedback provided by the VMUs to the VSPs regarding the content they receive and are managed utilizing a subjective logic model. Ultimately, we validate our proposed model using comprehensive simulations. Our key findings indicate that our mechanism effectively prevents the initiation of poisoning attacks by malicious SIoT devices. Additionally, our system ensures that reliable SIoT devices, previously misclassified, are not barred from participating in future rounds of the market. T

关键词： Deep reinforcement learning

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A hybrid boundary integral-PDE approach for the approximation of the demagnetization potential in micromagnetics

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Advances in Computational Mathematics 2025年第3期51卷 1-26页

作者： Arjmand, Doghonay Calzada, Víctor Martínez Department of Information Technology Division of Scientific Computing Uppsala University Uppsala Sweden Departamento de Ingeniería Aeronáutica Universidad Politécnica Metropolitana de Hidalgo Hidalgo México

The demagnetization field in micromagnetism is given as the gradient of a potential that solves a partial differential equation (PDE) posed in $$\mathbb {R}^d$$ . In its most general form, this PDE is supplied with continuity condition on the boundary of the magnetic domain, and the equation includes a discontinuity in the gradient of the potential over the boundary. Typical numerical algorithms to solve this problem rely on the representation of the potential via the Green’s function, where a volume and a boundary integral terms need to be accurately approximated. From a computational point of view, the volume integral dominates the computational cost and can be difficult to approximate due to the singularities of the Green’s function. In this article, we propose a hybrid model, where the overall potential can be approximated by solving two uncoupled PDEs posed in bounded domains, whereby the boundary conditions of one of the PDEs are obtained by a low cost boundary integral. Moreover, we provide a convergence analysis of the method under two separate theoretical settings: periodic magnetization and high-frequency magnetization. Numerical examples are given to verify the convergence rates.

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